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Overview

Lectures:

Mondays 8-10, MH:309A
Thursdays 15-17, MH:309A
See also the schedule below.

Computer exercises:

Tuesdays 15-17
or
Wednesdays 15-17

Computer exercises are in MH:230 except on On February 1, February 9, and February 16 when the the exercises are in MH:231 instead.

For those of you who are new to matlab:
Introduktion till Matlab (in Swedish)
Introduction to Matlab (Mathworks)
Matlab to R reference

Office hours:

Monday 10:15-11:30, MH:241
Wednesday 13:15-15:00, MH:241

Examination

Five computer exercises, each requiring a very short report (no longer than one page).
Three home assignments/projects, with oral examination after the last project.
The assignments will be handed out during the 2:nd, 4:th and 6:th course week.

Project 1: Simulation and Monte-Carlo integration, (pdf)
Handed out 2011-01-24, due 2011-02-10.

Project 2: MCMC and Bayesian inference, (pdf)
Data: challenger.txt, coal_mine.txt
Handed out 2011-02-07, due 2011-02-24.

Project 3: EM alogirithm and Bootstrap tests, (pdf)
Data: gene_data.txt, telecom_data.txt
functions: gibbsHMM.m, simulateHMM.m
Handed out 2011-02-24, due 2011-03-10.

The third project has now been graded, you can get them in my office.

Oral exam

Times for the oral exam are

• 11/3: 13:15-14:00, MH:330
• 14/3: 10:15-11:00, 11.00-11.45, 13.15-14.00,14.00-14.45, MH:330
• 21/3: 10:15-11:00, 11.00-11.45, 13.15-14.00,14.00-14.45, MH:330
• 22/3: 13:15-14:00, MH:330
• 23/3: 13:15-14:00, MH:330

You sign up for a time on the following link: Sign up.
You find MH:330 on the third floor in the Mathematics building.

Schedule

Day				Lectures (chapters in the book)	Computer Ex.	PDF
v1	Mon	17/1	L1	Introduction Examples		pdf
	Thu	20/1	L2	Random number generation (2-3) Uniform random numbers Ziggurat algorithm		pdf
v2	Mon	24/1	L3	Monte Carlo-integration (4)		pdf
	Tue	25/1	C1		pdf
	Wed	26/1	C1
	Thu	27/1	L4	MCMC (5.1,5.3,5.5-5.6) Two key papers on MCMC: Metropolis et al. (1953) Hastings (1970) Extra material about MCMC: pdf Matlab code for example: example.m		pdf
v3	Mon	31/1	L5	MCMC (5.2,5.4,5.7) Matlab code for Slice sampler example: slicesampler.m		pdf
	Tue	1/2	C2		pdf
	Wed	2/2	C2
	Thu	3/2	L6	Stochastic modelling and Bayesian inference (6.1,10.1) Matlab code for example: MCMC_Exp.m		pdf
v4	Mon	7/2	L7	Bayesian examples, simulation (10.2-10.3,11)		pdf
	Tue	8/2	C3		pdf, data
	Wed	9/2	C3
	Thu	10/2	L8	Statistical models (6)		pdf
v5	Mon	14/2	L9	Bootstrap (7.1-7.3) A leisurely look at the Bootstrap, the Jackknife, and Cross-Validation (1983)		pdf
	Tue	15/2	C4		pdf, stars.txt
	Wed	16/2	C4
	Thu	17/2	L10	Parametric Bootstrap (7.4-7.5)		pdf
v6	Mon	21/2	L11	Permutaion test (8)		pdf
	Tue	22/2	C5		pdf, atlantic.txt est_gumbel.m
	Wed	23/2	C5
	Thu	24/2	L12	The EM-algorithm (9) Maximum Likelihood from Incomplete Data via the EM Algorithm (1977)		pdf
v7	Mon	28/2	L13	Summary, comments		pdf
	Tue	1/3	C6		Help with project 3
	Wed	1/3	C6		Help with project 3

Literature

M. Sköld, Computer Intensive Statistical Methods and some additional handouts.
The book is available from the Department for Mathematical Statistics.

Some known misprints in the course book can be found in the Errata.

The above book is the only one needed for the course.
However if you wish to explore other literature some good options are:

Monte Carlo

• Markov Chain Monte Carlo in Practice, Gilks, Richardson and Spiegelhalter, 1996
• Monte Carlo Statistical Methods, Robert and Casella, 2005

Bootstrap

• Bootstrap Methods and Their Application, Davison and Hinkley, 1997
• An Introduction to the Bootstrap, Efron and Tibshirani, 1994

People

Course administrator/lecturer:

David Bolin
room: MH:241
phone: 046-222 79 74
e-mail: bolin@maths.lth.se

Computer exercises:

Johan Lindström
room: MH:245
e-mail: johanl@maths.lth.se